Multi-vehicle settings

ABSTRACT

An computing device is configured to detect that a user device is approaching the vehicle. An identifier for the user device and an identifier for the vehicle is transmitted to a remote server. A model is used to generate settings data in the vehicle, wherein the model is generated at least in part based on the identifier for the user device and the identifier for the vehicle. At least one setting for the at least one component in the vehicle is generated according to the model, and is applied to the at least one component in the vehicle.

BACKGROUND

A vehicle operator, e.g., a driver of a passenger car, may prefer and/orfind beneficial certain vehicle settings. To take just a few examples,in a given vehicle, different settings for a sensitivity of a throttle,a brightness of lights, radio station presets, minor positions, seatpositions, etc., may be preferred and/or beneficial for variousoperators. Further, a given vehicle operator may use a variety ofvehicles, e.g., rented or leased vehicles, vehicles in a car-sharingsystem, etc. For example, systems exist for vehicle-sharing in which anoperator may select entirely different vehicles for different trips,e.g., a compact sedan for a city trip, and a pickup truck for a trip tohaul lumber. The vehicle operator may prefer and/or find beneficialvarious settings in each of the vehicles. However, various vehicles havedifferent configurations with respect to many settings, e.g., throttlesensitivity, seat positions, etc. Mechanisms are presently lacking forusing an operator's preferred settings with respect to a first vehiclein a second vehicle that is configured differently.

DRAWINGS

FIG. 1 a block diagram of an exemplary system for providing vehiclesettings.

FIG. 2 illustrates details of operations of a predictor module.

FIGS. 3 a and 3 b illustrate an example of models 210 for throttlemappings.

FIG. 4 is a diagram of an exemplary process for specifying vehiclesettings.

FIG. 5 is a diagram of an exemplary process related to certain detailsfor generating vehicle settings.

DETAILED DESCRIPTION System Overview

FIG. 1 is a block diagram of an exemplary system 100 for providingvehicle settings. It should be understood, however, that systems andmethods disclosed herein could be, and are intended to be, applicable toequipment other than vehicles, e.g., boats, airplanes, motorcycles, etc.

In any case, with respect to examples discussed herein, a plurality ofvehicle computers 105 are each disposed in a vehicle, and each includesone or more processors as well as at least one memory that storescollected data 110 concerning an operator's use of the vehicle, alongwith settings data 115 concerning vehicle settings preferred by and/orbeneficial for a vehicle operator. A vehicle generally includes one ormore data collectors 120, e.g., sensors, input devices, etc., includedin or communicatively coupled to a vehicle computer 105 in the vehicle.The vehicle computer communicates, generally via a network 125, with aserver 130. The server 130 includes a processor and a memory, the memorygenerally storing program instructions including a classifier module 135and a predictor module 140, as well as collected data 110, vehicle data145, and profile data 150, which in turn may be used to generatesettings data 115 that may be provided to a vehicle computer 105. Thesystem 100 may further include one or more user devices 155 that may beused to communicate with the vehicle computer 105 in various ways suchas described herein below.

Exemplary System Elements

Vehicle computer 105 generally includes a processor and a memory, thememory including one or more forms of computer-readable media, andstoring instructions executable by the processor for performing variousoperations, including as disclosed herein. The memory of the computer105 further generally stores collected data 110 and settings data 115.The computer 105 is generally configured for communications on acontroller area network (CAN) bus or the like. Via the CAN bus and/orother mechanisms, e.g., Bluetooth or other wireless technologies, thecomputer 105 may transmit messages to various devices in a vehicleand/or receive messages from the various devices, e.g., controllers,actuators, sensors, etc., including data collectors 120.

Collected data 110 may include a variety of data collected in a vehicle.In general, collected data 110 may include any data that may be gatheredby a collection device 120, and that may be relevant to settings 115.For example, collected data 100 may include vehicle speed, acceleration,deceleration, tire pressures, seat positions, minor positions,windshield wiper usages, etc.

In particular, collected data 110 may include what are sometimesreferred to a context parameters, e.g., data relating to a vehicle'scurrent operation, environment, etc. Examples of collected data 110include tire friction coefficient value (derived by traction controlsystem and/or anti-lock brake (TCS/ALB) modules), vehiclespeed/bearing/position (e.g., via a Global Positioning System (GPS)module included in or connected to computer 105), ambient lights, e.g.,(daylight/sunset/night/sunrise calculated with vehicle pyrometer), localtopography (using a global information system (GIS) server), cloud cover(using a weather server), ambient temperature (vehicle outdoortemperature sensor data collector 120), precipitation (windshield datacollector 120 and/or weather server), etc.

As will be understood, much of the foregoing is generally available,directly or indirectly, from signals on a vehicle bus. For example, withrespect to determining a friction coefficient, a maximum possible can beestimated from environmental conditions such as temperature, barometricpressure, tire pressure, etc. An actual friction coefficient may bemeasured during hard acceleration and deceleration of a vehicle by acontrol algorithm that uses wheel speed, wheel torque, and accelerationmeasurements to determine the ratio between peak torque and vehiclemass, which is the friction coefficient.

Settings 115 may include virtually any information concerning a settingin a vehicle. A list of examples of settings 115 is provided below inAppendix A. Further, the examples of settings 115 disclose examples ofcollected data 110 that may be used for a setting 115. For example,collected data 110 may include a vehicle operator's weight detected by aweight sensor, whereupon settings data 115 may indicate whether avehicle airbag should be enabled or disabled. Likewise, collected data110 may record an operator's preference with respect to windshield wiperintervals under various precipitation conditions, whereupon settingsdata 115 may include an indication of windshield wiper settings undervarious respective precipitation conditions.

Certain collected data 110 may not be directly mappable to settings 115.For example, collected data 110 may include an operator's driving habitswith respect to acceleration and braking, where such collected data maybe relevant to the operator's preferences with respect to throttlesensitivity. However, throttle sensitivity in a first vehicle, e.g., asports car, may not directly translate to a throttle sensitivity in asecond vehicle, e.g., a heavy sport-utility vehicle. Likewise, settings115 to facilitate speech recognition in a first vehicle, e.g.,microphone sensitivity settings 115, rules for noise cancellation, etc.,may differ from the settings 115 appropriate for a second vehicle.Accordingly, as described further below, mappings and/or translationsmay be utilized so that collected data 110 and/or settings data 115 froma first vehicle may be transformed to settings data 115 for an operatorin a second vehicle, and/or that generic settings 115 (e.g., as may beembodied in the universal model 205 discussed below with respect to FIG.2) may be transformed into settings 115 for one or more vehicles.

Data collectors 120 may include a variety of devices, including varioussensors in a vehicle. For example, a data collector 120 may include aweight sensor, as mentioned above, as well as sensors that record lightconditions, audio sensing devices, etc. Further, a data collector 120may receive data via a communication mechanism such as a vehicle CANbus, e.g., data regarding a vehicle velocity, acceleration anddeceleration, transmission states, states of various vehicle componentssuch as doors, windows, climate control systems, seats, lights, tirepressure, etc. More complete examples of data that may be provided by adata collector 120 may be inferred from the exemplary list of settings115 provided in Appendix A.

The network 125 represents one or more mechanisms by which a vehiclecomputer 105 may communicate with a remote server 130. Accordingly, thenetwork 125 may be one or more of various wired or wirelesscommunication mechanisms, including any desired combination of wired(e.g., cable and fiber) and/or wireless (e.g., cellular, wireless,satellite, microwave, and radio frequency) communication mechanisms andany desired network topology (or topologies when multiple communicationmechanisms are utilized). Exemplary communication networks includewireless communication networks, local area networks (LAN) and/or widearea networks (WAN), including the Internet, providing datacommunication services. Further, as mentioned above, the network 125 mayinclude a CAN bus on one or more vehicles.

The server 130 may be one or more computer servers and/or databases,generally including at least one processor and at least one memory, thememory storing instructions executable by the processor, includinginstructions for carrying out various of the steps and processesdescribed herein. Such instructions include instructions in variousmodules such as a predictor module 135 and a classifier module 140. Itis to be understood that the server 130 could represent hardware and/orsoftware in more than one physical location. For example, the server 130could represent a “Web Service” in a content delivery network on aservice bus, such as is known; processing and memory of the server 130could be distributed.

The predictor module 135 includes instructions for employing predictivemodeling techniques to generate settings 115 for an operator of avehicle, e.g., based on collected data 110 and/or settings data 115related to other vehicles. The predictor module 135 may further includeclassifiers 140, which use may include models such as neural networksthat accept as input vehicle data 145, profile data 150, as well ascollected data 110 and historical settings data 115, to determine datafor use by the predictor module 135 with respect to generating newsettings 115 for an operator for a particular vehicle. Further detailsof operations of the server 130 utilizing the predictor module 135 andclassifiers 140 are discussed below with respect to FIG. 2.

Vehicle data 145 generally includes data relating to parameters in avehicle. Such parameters may include a variety of data relating tovehicle dimensions, possible configurations, etc. For example, data 145pertaining to a particular vehicle make and model may specify a heightof a vehicle roof, a vehicle length and width, a vehicle weight,information about a vehicle transmission, information about a vehicleengine, etc. Further, vehicle data 145 may include information aboutenvironmental conditions within a vehicle, e.g., acoustic conditionssuch as the effect of road noise in a vehicle, etc.

Examples of vehicle data 145 that can be used as a parameter in aclassifier 115, as discussed below, include without limitation avehicle's power/weight ratio, body style, color, optional equipmentpackage, possible brake, clutch, and/or accelerator pedal positions,possible seat positions, possible microphone and/or speaker positions(as will be known to those skilled in the art, all the foregoing cangenerally be determined according to Vehicle Identification Number (VIN)by querying a vehicle data server), vehicle's acoustic characteristics(generally determinable from microphone and echo cancellationparameters).

Profile data 150 generally includes data relating to a user of thesystem 100, e.g., a vehicle operator. Like collected data 110, vehicledata 145, profile data 150 may be used as input to one or moreclassifiers 115. For example, profile data 150 may include informationabout a vehicle operator's preferences relating to various vehicleparameters, e.g., climate settings, throttle sensitivity, windshieldwiper operation, lighting conditions, etc. Accordingly, profile data 150may be provided to the server 130 via a graphical user interface (GUI)or the like provided by the server 130 to a user device 155. Further,profile data 150 may be generated from collected data 110. For example,collected data 110 may include information concerning acceleration anddeceleration patterns of a vehicle operator. This information may beincluded in profile data 150 related to throttle sensitivity settings115 appropriate for the vehicle operator.

Further examples of profile data 150 that can be used as a parameter toa classifier 140 such as discussed below include without limitationoperator body dimensions and related parameters (height, weight(determined from user inputs or a weight sensing data collector 120),arm length, leg length, preferred seat and steering wheel positions in agiven vehicle, camera images, user inputs regarding preferences), yearsdriving (determined from a user input), vehicle type (e.g., model) theoperator first learned to drive in (determined from a user input),typical commuting route, and/or voice characteristics (pitch, nasal,speech rate, dialect, age, e.g., determined by a voice learning system),etc.

A user device 155 may be any one of a variety of computing devicesincluding a processor and a memory, as well as communicationcapabilities. For example, the user device 155 may be a portablecomputer, tablet computer, a smart phone, etc. that includescapabilities for wireless communications using IEEE 802.11, Bluetooth,and/or cellular communications protocols. Further, the user device 155may use such communications capabilities to communicate via the network125 and also directly with a vehicle computer 105, e.g., usingBluetooth.

Settings Generation

FIG. 2 illustrates further details of operations of the predictor module135, which as just mentioned may be used to generate settings 115 for avehicle operator for various types of vehicles. Accordingly, thepredictor module 135 employs a universal representation model 205 ofvehicle settings 115 for an operator. That is, the model 205 representsvarious vehicle settings 115 for the operator without regard to a typeof vehicle. For example, as shown in FIG. 2, classifiers 140 may be usedto generate vehicle settings models 210 that in turn may be used togenerate settings data 115 from the universal representation model 205for a an operator for a specific type of vehicle.

In general, a universal model 205 includes a generic set of settings 115related to one or more features, elements, components, etc. of a vehiclefor a vehicle operator. For example, a model 205 may specify genericsettings for a seat position, mirror position, etc. Further, the exampleof throttle sensitivity profile data 150, mentioned above, isillustrative of how the universal representation model 205 may be abasis for translating settings 115 for a vehicle operator from one ormore first vehicles to a second vehicle. Table 1 shows an exemplaryportion of a universal representation model 205 for throttle settings115:

TABLE 1 Pedal Position Requested Torque 0 0 1 16.30969042 2 29.572602713 41.46107582 4 52.34222439 5 62.40482266 6 71.76953572 7 80.5226789 888.73037926 9 96.44566495 10 103.7124527 11 110.5679864 12 117.044424913 123.1699301 14 128.9694406 15 134.465243 16 139.6774032 17144.6241012 18 149.3218956 19 153.7859366

By way of further elucidating this example, consider that, presently,vehicles generally include a so-called throttle-by-wire mechanism inwhich the vehicle has a data collector 120, e.g., a sensor, that detectsa position of a vehicle throttle, e.g., an accelerator pedal. Asillustrated in Table 1, the throttle position may be mapped to arequested torque associated with the throttle position, e.g., using apre-defined curve typically generated as part of a vehicle's drivabilitydesign. However, a throttle mapping need not be set for the entire lifeof a vehicle, but instead may be re-programmed, e.g., to suit operatorpreferences, driving context, etc. For example, under slipperyconditions a less aggressive throttle map might be preferred, e.g., acoefficient of friction could be estimated by a computer 105 in avehicle and the estimate may be used to automatically change thethrottle map to meet the driver's preferences, etc.

An “aggressive” throttle mapping will generally include a large increaseof torque as throttle moves through the lower half of its range, andthen a smaller increase in torque as the throttle moves through an upperrange. Such a throttle mapping provides the feeling of rapidacceleration, i.e., a powerful launch from a stop that is preferred bysome vehicle operators. Other vehicle operators prefer a more graduallaunch, and potentially better fuel economy, vehicle wear, etc., andwould prefer to only have high torque when needed for passing oraccelerating onto a highway.

Accordingly, an analytic curve model 210 may be used to map throttlepositions to requested torques, or other mechanisms, such as a splinecurve or an interpolation of data points may be used for a model 210. Ananalytic curve requires less memory to store mapping than do splines andinterpolations, but require more computational power to compute.Further, in some cases an analytic function may be useful for a model210 because it has infinite continuous derivatives.

FIGS. 3A and 3B respectively illustrate exemplary mathematical models210-3 a and 210-3 b for determining a torque τ that will be applied atvarious throttle pedal positions, denoted by ρ. With respect to FIG. 3A,α and β are fit parameters that various iterations of classifiers 140may modify to improve settings 115 generated from the model 210.

A classifier 140 may take a number of factors into account for selectinga model 210, e.g., translating throttle settings 115 may include profiledata 150 such as parameters for driver preferences, and could alsoinclude context parameters such as driving conditions (e.g., estimatedcoefficient of friction) as mentioned above. Further, context parametersrelevant to a translation model 210 could include vehicle data 145concerning vehicle features such as pedal pressure and the horizontaldistance from the pedal to the steering wheel. Inclusion of thesefeatures in the as input to a classifier 140 allows for prediction of anoperator's preferred throttle map for a specific vehicle that theoperator may use, e.g., a specific vehicle in a fleet or car-sharegroup. Thus, a classifier 140 can use parameters in profile data 150,vehicle data 145, etc. such as operator and/or vehicle dimensions topredict a position of an operator in different vehicles, and consequentchanges in a throttle map.

Accordingly, parameters for a classifier 140, such as for selecting amodel 210 for translating a first throttle mapping to a second throttlemapping may be developed using machine-learning techniques. Such alearning process may occur in at least one of two modes, first,incremental variation, and second, transformational change.

Variational changes generally affect a small number of classifier 115model parameters at one time and generally initially would, inquantitative terms, be on the order of a few percent and moreover,generally become smaller as the model becomes more refined. Variationalchanges come in two forms, first, system initiated, and second, userinitiated. System initiated learning gathers profile data 150 bycollecting user, e.g., vehicle operator, input, and may be analogized towhat an eye doctor does to fit glasses. A change may be made to a systemparameter, e.g., throttle sensitivity, and then a vehicle operator maybe surveyed, e.g., using an application on a mobile device, using ahuman-machine interface, e.g., a touchscreen, in a vehicle, etc., todetermine whether the operator likes or dislikes the change. Thevariations are implemented in the classifier 115 model if they improvedriver satisfaction and may be used to direct future variational changeif it is desired to avoid repeating the same variations in the future.With user initiated learning, in contrast, an operator may request aparameter change, e.g., “make the throttle more sensitive at lowspeeds,” whereupon computer 105 modifies a throttle mapping; further, auser interface such as mentioned above may be used to receive inputindicating whether a driver prefers the new settings or the old. If thenew are preferred, then these are used, and old setting may be retainedto direct future incremental changes.

Exploratory learning may be accomplished by making transformationalchanges in model parameters to see if a vehicle operator prefers old ornew, i.e., changes, parameters. The large changes are abstracted fromprofile data 150 for other vehicle operators, or possibly frompredetermined settings. The purpose of the exploratory change is toaccommodate paradigm shifts in operator preferences and throttle mapsthat the driver may not anticipate liking.

FIGS. 3 a and 3 b illustrate an example of models 210 for throttlemappings. As illustrated in FIG. 3, first and second vehicles may userespective first and second analytic functions, e.g., models 210-3 a and210-3 b, to define throttle mapping. To address such differences inmathematical models 210, model transformation(s) may be used totranslate each of the respective first and second curves to and from auniversal math model (an interpolation table) such as may be stored in adata store of the server 130.

Another example of using classifiers 140 to generate models 210 could betaken from the realm of speech recognition, e.g., settings 115 tosupport a spoken dialog system in a vehicle. A vehicle's spokendialog-based subjective command control of vehicle systems (SCC)generally includes profile data 150 being accumulated over time, andstored for voice recognition and speech synthesis. An SCC dialog can bemodeled as a transfer of information between an SCC-meaning language anda human-meaning language through a noisy channel, e.g., VauquoisTriangle, as is known. When a user speaks to the SCC, themeaning-language is translated in the user's brain into a semanticlanguage, then into syntactic structure, then phonetic language, theninto movements of muscles that produce sound from the user's mouth in avehicle, where the sound is modified by the acoustics of the vehiclebefore being picked up by a microphone data collector 120 that furthermodifies the sound before it is digitized. The SCC likewise has aninverse process in which the acoustics of the car are first deconvolvedthrough signal processing, formants corresponding to muscle movementsextracted, words and syntactic structure hypothesized, with semanticstructure determined and meaning determined therefrom. Noise factors areintroduced at each stage that are either characteristic of the driver orof the vehicle and must be learned by the speech recognition system.

Certain types of agreements are also needed between the driver and theSCC for the communication to be effective and enjoyable, such as thelanguage/dialog that will be used and pragmatic considerations (e.g.,Grice's Maxims, as is known). Certain environmental considerations mustbe accepted and adapted to such as the acoustics of the vehicle and thepitch of the member's voice, etc.

Accordingly, at various stages of a speech recognition process,classifiers 140 may be used to accept various parameters for generatingmodels 210. For example, profile data 150 may be generated concerning aparticular operator's speech patterns, vehicle data 145 could relate tobackground noise in a vehicle, etc. For example, most of the time amicrophone data collector 110 is simply collecting background noisebecause no one is speaking to the computer 105. An acoustic system cancharacterize this noise over time with an acoustic model and implement afilter for that particular type of noise. As is known, one model is 1/for inverse frequency, and another is 1/f² or inverse frequency squared.Further, the models may be more complex, and may have inputs such asvehicle speed, tire pressure, tire age, wind speed, engine rpm, torquerequest, road surface condition, temperature, air density, windowsettings, etc. Many of these inputs are found on a vehicle network,e.g., a CAN bus or the like. An acoustic system's model may also haveinputs like dimensions of the cockpit of the vehicle, position of themicrophone, acoustic properties of the seats, etc. that are related tothe construction of the vehicle. Other inputs to the model can be drivercharacteristics such as height and seat position.

In addition to characterizing background noise, an acoustic system mayoccasionally emit a known sound through the speakers such as a chirp,and analyze the difference between a Fourier transform of the chirp andthe sound received by the microphone. This can be used to develop aninverse filter that amplifies and removes distortion from intentionalsounds in the vehicle while the noise filter removes unintentionalbackground noise to enhance speech recognition.

Having tuned acoustic system filters over time it is possible toestimate filter parameters for a vehicle operator under differentenvironmental conditions, type of vehicle, speed, etc. Such estimate mayfurther be improved by using known sounds such as chirps as describedabove, or from a quality of the speech recognition made over manymeasurements. This could be done with a Kalman filter which is a simplelearning system, or possibly with a neural network that is a morecomplex system. Similarly, a model can be developed to predict acousticmodel parameters for a driver in a different vehicle, or a differentdriver in a specific vehicle. Implementing these types of estimatesrequires the interconnectivity of the Internet.

Exemplary Process Flows

FIG. 4 is a diagram of an exemplary process 400 for specifying vehiclesettings.

The process 400 begins in a block 405, in which a user device 155 isregistered with the server 130. In general, a device 155 may beregistered with the server 130 when a user is preparing to use avehicle, e.g., a rental car, a vehicle in a fleet of vehicles, a vehiclein a car-sharing system, etc. The registration may be accomplished via avariety of mechanisms. For example, the user device 155 could access aGUI provided by the server 130, e.g., provided according to arepresentational state transfer (REST) architecture, to allow the userdevice 155 to log in, e.g., via a conventional username and password, tothe server 130. Alternatively, an application installed on the userdevice 155, e.g., a smart phone application utilizing a web service orthe like, could be configured to access the server 130 at a specifiedtime, when a user was at or near a specified location, etc. Further, viathe GUI, via a smart phone application, etc., a user could specify avehicle that the user was planning to be used, or, based on identifyinga user, the server 130 could access a user reservation or the like toidentify a vehicle to be used. Registration of a user device 155generally includes storage of a unique or substantially uniqueidentifier for the device 155 being stored in a data store of the server130.

Next, in the block 410, the server 130 determines that the user device155 is approaching or proximate to, i.e., within a predetermineddistance of, a vehicle to be used. For example, the user device 155 mayinclude hardware and software to use the known global positioning system(GPS), and may communicate GPS data to the server 130. Further forexample, an IEEE 802.11 wireless transceiver on a vehicle, which may bein a mode such as access point, promiscuous or monitor, may detect thesmart phone when it comes into range (20 to 100 meters), or similarly asmart device could be configured to be detected by a Bluetooth wirelesstransceiver on a vehicle, e.g., at a distance of 5 to 20 meters.Alternatively, the computer 105 could detect the approach by the userdevice 115, via cellular technology, IEEE 802.11, etc., and couldcommunicate to the server, e.g., via the network 125, that the userdevice was proximate to and/or approaching the vehicle. In any event,the server 130 and/or the computer 105 will generally determine, and useas appropriate in various messages, the identifier associated with theuser device 155. Further, the computer 105 may provide a unique orsubstantially unique identifier for a vehicle, or at least for a type(e.g., brand, model, trim level, etc.) of a vehicle.

Next, in a block 415, the server 130 determines initial settings 115 forthe vehicle being approached by the user of the device 155. For example,the server 130 may use profile data 150 and vehicle data 145 todetermine certain settings 115 e.g., seat positions, mere positions,transmission configurations, etc. Further, the predictor module 135 maybe configured to generate default models 210 for respective vehicletypes for a vehicle operator based on the universal representation model205 for a vehicle type, and/or default models 210 may be provided forsome or all of the settings 115 in a particular type of vehicle.

Next, in a block 420, if the block 420 is reached in a first iterationof the process 400, the server 130 sends the settings 115 determined inthe step 415 to the vehicle computer 105. In subsequent iterations ofthe process 400, the server 130 sends settings 115 determined asdescribed below with respect to the blocks 425-435.

Next, in a block 425, settings 115 transmitted in the block 420 areapplied in a vehicle, e.g., a vehicle computer 105 may send instructionsvia a CAN bus to various controllers and/or actuators in a vehicle suchas a controller of seat positions, minor positions, a transmissioncontroller, a controller of environmental settings, etc.

Next, in a block 430, during operation of a vehicle, the vehiclecomputer 105 collects data 110. For example, as mentioned above, datacollectors 120 may include sensors or the like that collect data 110during vehicle operation. Further, profile data 150, e.g., via userinput, etc. may be collected as described above. Accordingly, contextdata such as vehicle speed, acceleration and deceleration, settings ofenvironmental controls, lighting, usage of windshield wipers, etc. maybe collected, and transmitted to the server 130.

Next, in a block 435, the server 130, using the predictor module 135,e.g., using classifiers 140 to update models 210 as described above,updates and/or generates models 210 to generate and/or update settings115 being used by the vehicle computer 105.

Next, in a block 440, the server 130 determines whether to continue theprocess 400, e.g., whether the server 130 is able to continuecommunications with the vehicle computer 105. If so, control returns tothe block 420. Otherwise, control proceeds to the block 445.

Following the block 440, the process 400 ends.

FIG. 5 is a diagram of an exemplary process 500 related to certaindetails for generating vehicle settings 115.

The process 500 begins in a block 505, in which the server 130 retrievesa set of collected data 110 for a user, vehicle data 145 for a vehiclethat the user is expected to operate (sometimes referred to as thetarget vehicle), and/or profile data 150 for the user.

Next, in a block 510, the predictor module 135 classifies the dataobtained in the block 505, e.g., according to various classes ofvehicles and/or classes of users. For example, data 110 may beclassified to determine if it is relevant for a target vehicle, e.g.,data 110 related to a user's history with subcompact cars may berelevant where the target vehicle is a subcompact or compact car, butnot to instances where the target vehicle is a sport-utility vehicle orthe like.

Likewise, in a block 515, which may follow the block 510, data 110 fromother users is classified, inasmuch as such data may be relevant if theother users have physical attributes similar to the present user ofinterest, e.g., a similar weight and height, but otherwise may not berelevant. In any event, classification criteria may be supplied to theclassifier 140, and data may be segmented or classified in the block 510to promote use of more relevant data in building a predictive model forgeneration of settings 115.

Next, in a block 520, the server 130 retrieves settings 115 from a priorvehicle or vehicles operated by the user of current interest and/or theuniversal representation model 205.

Next, in a block 525, the settings 115 from the prior vehicle orvehicles and/or the universal representation model 205 are used asparameters to classifiers 140 to generate model 210 that in turn may beused to generate a set of current settings 115 for the target vehicle.In general, specific classifiers 140 may be used to optimize settings115, such as a throttle mapping, to a specific operator's preferencesgiven a set of context conditions, e.g., road friction. When conditionschange, it is generally necessary to identify model parameters that needto be modified to implement a specific type of change. In the example ofthe throttle map, a change in road conditions, or a change in drive type(e.g., two-wheel to four-wheel) could require a modification of theaggressiveness of the throttle map. Then, when an operator goes from onevehicle to another within a same driving context, changes in thethrottle map may maintain a generally consistent throttle mapping inspite of differences between vehicles. Further, note that parametersindicating changes in weather and drive type are classified asnon-vehicle related context parameters and those related to the vehiclesuch as peddle spring tension and seat position are vehicle relatedparameters.

Next, in a block 530, settings 115 for the target vehicle are generatedfrom the model or models 210 obtained in the block 525.

Following the block 530, the process 500 ends.

In general, steps of processes disclosed herein may be performed by theserver 130 and/or the in-vehicle computer 105. In particular, at leastsome operations described herein to the server 130 could be performed bythe in-vehicle computer 105, e.g., operations of the predictor module135. Likewise, certain operations that may generally be performed by thecomputer 105, e.g., generating settings 115 as discussed with respect tothe block 530 of the process 500, could additionally or alternatively beperformed by the server 130, which could then transmit settings 115 tothe vehicle computer 105, e.g., via the network 125.

CONCLUSION

Computing devices such as those discussed herein generally each includeinstructions executable by one or more computing devices such as thoseidentified above, and for carrying out blocks or steps of processesdescribed above. For example, process blocks discussed above may beembodied as computer-executable instructions.

Computer-executable instructions may be compiled or interpreted fromcomputer programs created using a variety of programming languagesand/or technologies, including, without limitation, and either alone orin combination, Java™, C, C++, Visual Basic, Java Script, Perl, HTML,etc. In general, a processor (e.g., a microprocessor) receivesinstructions, e.g., from a memory, a computer-readable medium, etc., andexecutes these instructions, thereby performing one or more processes,including one or more of the processes described herein. Suchinstructions and other data may be stored and transmitted using avariety of computer-readable media. A file in a computing device isgenerally a collection of data stored on a computer readable medium,such as a storage medium, a random access memory, etc.

A computer-readable medium includes any medium that participates inproviding data (e.g., instructions), which may be read by a computer.Such a medium may take many forms, including, but not limited to,non-volatile media, volatile media, etc. Non-volatile media include, forexample, optical or magnetic disks and other persistent memory. Volatilemedia include dynamic random access memory (DRAM), which typicallyconstitutes a main memory. Common forms of computer-readable mediainclude, for example, a floppy disk, a flexible disk, hard disk,magnetic tape, any other magnetic medium, a CD-ROM, DVD, any otheroptical medium, punch cards, paper tape, any other physical medium withpatterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any othermemory chip or cartridge, or any other medium from which a computer canread.

In the drawings, the same reference numbers indicate the same elements.Further, some or all of these elements could be changed. With regard tothe media, processes, systems, methods, etc. described herein, it shouldbe understood that, although the steps of such processes, etc. have beendescribed as occurring according to a certain ordered sequence, suchprocesses could be practiced with the described steps performed in anorder other than the order described herein. It further should beunderstood that certain steps could be performed simultaneously, thatother steps could be added, or that certain steps described herein couldbe omitted. In other words, the descriptions of processes herein areprovided for the purpose of illustrating certain embodiments, and shouldin no way be construed so as to limit the claimed invention.

Accordingly, it is to be understood that the above description isintended to be illustrative and not restrictive. Many embodiments andapplications other than the examples provided would be apparent to thoseof skill in the art upon reading the above description. The scope of theinvention should be determined, not with reference to the abovedescription, but should instead be determined with reference to theappended claims, along with the full scope of equivalents to which suchclaims are entitled. It is anticipated and intended that futuredevelopments will occur in the arts discussed herein, and that thedisclosed systems and methods will be incorporated into such futureembodiments. In sum, it should be understood that the invention iscapable of modification and variation and is limited only by thefollowing claims.

All terms used in the claims are intended to be given their broadestreasonable constructions and their ordinary meanings as understood bythose skilled in the art unless an explicit indication to the contraryin made herein. In particular, use of the singular articles such as “a,”“the,” “said,” etc. should be read to recite one or more of theindicated elements unless a claim recites an explicit limitation to thecontrary.

APPENDIX Exemplary Settings

1. On-board computer settings

a. User interface settings that are comfortable for the user (e.g.,language, size and location of icons on a touchscreen, whether toinclude audio renderings, etc.)

b. User interface settings that are adapted to the vehicle (words forthings that have different meanings on different vehicles)

c. Acoustic parameters adapted to the vehicle/user combination

d. Information for communicating with mobile devices, e.g., viaBluetooth (also see #12 below)

2. Handles, Locks, Latches

a. Child door lock settings

b. Window lock settings

3. Heated backlights, outside mirrors, inside mirrors, windshield, etc.

a. Weather conditions under which these should be turned on and off

4. Instrument Panel and Controls

a. Location and appearance of major instruments on the adaptiveinstrument cluster

b. Location and appearance of warning lights

5. Key Illumination

a. Appearance and brightness of the key illumination lamps

6. Passenger restraints controls

a. Enable/disable and power of air bags

7. Power closure, power lock

a. Power sliding door/trunklid closure enabled/disabled

b. Sunroof/moonroof and sun screen positions

c. Power window positions

8. Power seat control

a. Forward/backward position, up/down position, lumbar support, elbowrests, firmness, seat back positions, etc.

9. Sun Visors

a. Illumination

b. Position

10. Wiper/Washer control

a. Wiper interval under a known precipitation condition.

b. Estimated washer usage under specific weather conditions for washerrange calculation

11. Climate control settings

a. Interior temperature and humidity preferences given sun load

b. Heat energy and water vapor produced by occupants

12. Cellular phone/tablet device

a. Personal contact list

b. Bluetooth pairing information

c. WiFi WPS password

d. Encryption certificates

e. MAC Address

Garage door opener

a. Codes and GPS locations for using each of the codes

14. Cruise Control

a. Preferred driving speeds given weather and user condition

b. Preferred headway distance given weather and user condition

c. Fuel economy preference

15. Keyless vehicle system

a. Vehicle access code

b. Transponder code

16. Navigation controls

a. Location of vehicle

b. Most likely destinations

c. Time, distance, aesthetic, and fuel economy preferences for a typicaltype of trip.

d. Road types, preferred transportation modes, willingness to accepttolls, robustness, etc.

e. Voice characteristics and display preferences.

17. Electronic engine and throttle control

a. Performance preferences

18. Acoustical Control Components

a. Vehicle sound preferences

19. Display/dialog preferences; Clock, Engine speed tachometer, FuelLevel Indication, Gauges/Warning Lights, Low Fuel/Warning Devices,Parking/Reversing Aid

a. Voice preferences; gender, age, speech rate, etc.

b. Brightness and appearance

20. Transmission

a. Preferred shift schedule

21. In-Car Entertainment

a. Radio station presets, genres

b. Bookmarks; audio books, podcasts, audio CD/DVD, advertisements etc.

c. Voice preferences, i.e. Gender, age, speech rate, etc.

d. Coupon wallet and electronic wallet data

22. External lighting:

a. Auxiliary lamps (Fog Lamps) on/off with low beams

b. Headlamps; when to lower beams, how to point around curves, colors,brightness

c. Reverse light brightness, beam pattern, light pattern, digitalmodulation codes

d. Tail light/Brake light pattern, color, intensity, digital modulationcodes

23. Courtesy Lighting

a. Permit for using courtesy lights

24. Four wheel steering;

a. Conditions under which to enable four wheel steering

b. Mode and intensity of four wheel steer that are preferred

25. Power steering

a. Force/Displacement gain curve, resisting force vs. speed curve,behavior near handling limit.

26. Load Leveling/Trailer Sway

a. Amount of load leveling needed for each trailer towed

b. Amount of sway control needed for each trailer towed

27. Tire pressure

a. Preferred tire pressure

28. Sound Inhibitors

a. Preferred vehicle sound map for speed, load, location and weatherconditions

1. A computer server that includes a processor and a memory, configuredto: receive an identifier for an operator of an equipment and anidentifier for the equipment; receive a set of parameters related tooperation of the equipment, the parameters including at least one ofoperator profile data, equipment data, and data collected from operationof the equipment; identify a universal model for at least one componentin the equipment; and generate a new model to determine least onesetting for the operator using the at least one component in theequipment; and transmit the new model to a computer in the equipment. 2.The computer of claim 1, further configured to: identify a previousmodel for settings of the at least one component; and use the previousmodel in generating the new model.
 3. The computer of claim 1, furtherconfigured to use a neural network to generate the new model.
 4. Thecomputer of claim 1, wherein the computer in the equipment is configuredto use the new model to determine the at least one setting.
 5. Thecomputer of claim 1, further configured to: receive a second set ofparameters related to operation of the equipment by the operator; anduse at least some of the second set of parameters to generate at asecond new model to determine least one setting for the operator usingthe at least one component in the equipment.
 6. The computer of claim 5,further configured to: transmit the second new model to the computer inthe equipment.
 7. The computer of claim 1, further configured to:receive a second set of parameters related to operation of the equipmentby the operator; use at least some of the second set of parameters togenerate at a second new model to determine least one setting for theoperator using the at least one component in a second equipment; andtransmit the second new model to a second computer in the secondequipment.
 8. The computer of claim 1, wherein the equipment is avehicle.
 9. A method, comprising: receiving an identifier for anoperator of an equipment and an identifier for the equipment; receivinga set of parameters related to operation of the equipment, theparameters including at least one of operator profile data, equipmentdata, and data collected from operation of the equipment; identifying auniversal model for at least one component in the equipment; andgenerating a new model to determine least one setting for the operatorusing the at least one component in the equipment; and transmitting thenew model to a computer in the equipment.
 10. The method of claim 9,further comprising: identifying a previous model for settings of the atleast one component; and using the previous model in generating the newmodel.
 11. The method of claim 9, further comprising using a neuralnetwork to generate the new model.
 12. The method of claim 9, whereinthe computer in the equipment is configured to use the new model todetermine the at least one setting.
 13. The method of claim 9, furthercomprising: receiving a second set of parameters related to operation ofthe equipment by the operator; and using at least some of the second setof parameters to generate at a second new model to determine least onesetting for the operator using the at least one component in theequipment.
 14. The method of claim 13, further comprising: transmittingthe second new model to the computer in the equipment.
 15. The method ofclaim 9, further comprising: receive a second set of parameters relatedto operation of the equipment by the operator; use at least some of thesecond set of parameters to generate at a second new model to determineleast one setting for the operator using the at least one component in asecond equipment; and transmit the second new model to a second computerin the second equipment.
 16. The method of claim 9, wherein theequipment is a vehicle.
 17. An in-vehicle computing device, comprising aprocessor and a memory, wherein the computing device is configured to:detect that a user device is approaching the vehicle; transmit anidentifier for the user device and an identifier for the vehicle to aremote server; receive, from the remote server, a model to generatesettings data in the vehicle, wherein the model is generated at least inpart based on the identifier for the user device and the identifier forthe vehicle; generate at least one setting for the at least onecomponent in the vehicle according to the model; and apply the at leastone setting to the at least one component in the vehicle.
 18. The deviceof claim 17, further configured to: collect data related to operation ofthe vehicle, and send the collected data to the remote server.
 19. Thedevice of claim 18, further configured to: receive a second model togenerate settings in the vehicle, generate at least one second settingfor the at least one component in the vehicle according to the secondmodel; and apply the at least one second setting to the at least onecomponent in the vehicle.
 20. The device of claim 17, wherein thecomponent is one of a seat, a minor, a throttle, a microphone, aspeaker, a climate control setting, a window, a sunroof, a moonroof, anda power steering control system.